/Brain_Stroke_Prediction_Using_Machine_Learning

Stroke is a disease that affects the arteries leading to and within the brain. A stroke occurs when a blood vessel that carries oxygen and nutrients to the brain is either blocked by a clot or ruptures. According to the WHO, stroke is the 2nd leading cause of death worldwide. Globally, 3% of the population are affected by subarachnoid hemorrhage, 10% with intracerebral hemorrhage, and the majority of 87% with ischemic stroke. 80% of the time these strokes can be prevented, so putting in place proper education on the signs of stroke is very important. The existing research is limited in predicting risk factors pertained to various types of strokes. Early detection of stroke is a crucial step for efficient treatment and ML can be of great value in this process. To be able to do that, Machine Learning (ML) is an ultimate technology which can help health professionals make clinical decisions and predictions. During the past few decades, several studies were conducted on the improvement of stroke diagnosis using ML in terms of accuracy and speed. The existing research is limited in predicting whether a stroke will occur or not. Machine Learning techniques including Random Forest, KNN , XGBoost , Catboost and Naive Bayes have been used for prediction.Our work also determines the importance of the characteristics available and determined by the dataset.Our contribution can help predict early signs and prevention of this deadly disease

Primary LanguageJupyter Notebook

Brainstroke Prediction

Kaggle dataset link : https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset

Libraries used for dataset processing

  • Numpy
  • Pandas

Libraries used for graphical representation

  • Matplotlib
  • Seaborn

Libraries used for Scaling and Oversampling

  • Sklearn.preprocessing
  • Imblearn

PREPROCESSING

  • Removed the id column – decreasing the dimension – did not add to insights in the data analysis.
df = df.drop(['id'],axis=1)
  • Count for NULL values are checked among the attributes of the dataset
print(df.isna().sum())
  • Only BMI-Attribute had NULL values
  • Plotted BMI's value distribution - looked skewed - therefore imputed the missing values using the median.
  • Didn’t eliminate the records due to dataset being highly skewed on the target attribute – stroke and a good portion of the missing BMI values had accounted for positive stroke

  • The dataset was skewed because there were only few records which had a positive value for stroke-target attribute

  • In the gender attribute, there were 3 types - Male, Female and Other. There was only 1 record of the type "other", Hence it was converted to the majority type – decrease the dimension

  • Most of the attributes in the dataset were binary values – converting the numeric bin values into string bin values for dummy encoding.

    • Dummy encoding similar to one-hot encoding – Values in the binary ecoded columns are 1/0 – Additional attributes/columns created.

  • Random oversampling done on the dataset to balance the skew in the target attributes.

    • Boosting the number of records in the minority class – records

EDA - Exploratory Data Analysis

  • Plotted plots of each attribute - Analyse trends if any – plots: pie, histogram.
  • Plotted relation of target attribute to other attributes to find any correlation.
  • Plotted the heatmap – correlation plot between the attributes.
    • Heatmap showed very less correlation between the attribute values.

MODEL BUILDING

  • Creating a train and test split of the oversampled dataset. (80-20)

Applied various Machine learning models for predictive analysis

  1. Decision tree
  2. KNN
  3. XG-Boost
  4. Random forest
  5. Logistic regression

Analysed the results generated using confusion matrix - accuracy, precision, recall and plotting the ROC plot and generating the AUC scores.

Accuracies calculated:

  1. Decision tree : 97.89%
  2. KNN : 97.22%
  3. XG-Boost : 97.48%
  4. Random forest : 99.48%
  5. Logistic regression : 76.34%

Chosen model - RANDOM FOREST

Results were validated using the k fold (20 splits) validation for overfitting

  • Accuracy: 95.01
    For Random Forest